{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>source</th>\n",
       "      <th>game_name</th>\n",
       "      <th>user_name</th>\n",
       "      <th>comment_time</th>\n",
       "      <th>content</th>\n",
       "      <th>score</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>qq_mobile</td>\n",
       "      <td>&amp;nbsp;杀神免费版</td>\n",
       "      <td>游客</td>\n",
       "      <td>1381762690</td>\n",
       "      <td>修复手机卡进行道具付费的漏洞！功能实用。速度很快。</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>qq_mobile</td>\n",
       "      <td>&amp;nbsp;杀神免费版</td>\n",
       "      <td>游客</td>\n",
       "      <td>1382515674</td>\n",
       "      <td>不错，不错这个游戏非常好玩随然登陆时间比较长但非常好玩的我看了一下登陆时间也差不多是一两分钟...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>qq_mobile</td>\n",
       "      <td>&amp;nbsp;杀神免费版</td>\n",
       "      <td>游客</td>\n",
       "      <td>1390121576</td>\n",
       "      <td>新区，推荐。我叫一杯凉茶。一起玩</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>qq_mobile</td>\n",
       "      <td>&amp;nbsp;杀神免费版</td>\n",
       "      <td>游客</td>\n",
       "      <td>1403781770</td>\n",
       "      <td>请问通过人物，能不能找到账号和所在区。</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>qq_mobile</td>\n",
       "      <td>&amp;nbsp;杀神免费版</td>\n",
       "      <td>游客</td>\n",
       "      <td>1404561529</td>\n",
       "      <td>经常卡死。经常卡死。经常卡死。经常卡死。响应很慢。响应很慢。响应很慢。响应很慢。响应很慢。响...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id     source    game_name user_name comment_time  \\\n",
       "0   1  qq_mobile  &nbsp;杀神免费版        游客   1381762690   \n",
       "1   2  qq_mobile  &nbsp;杀神免费版        游客   1382515674   \n",
       "2   3  qq_mobile  &nbsp;杀神免费版        游客   1390121576   \n",
       "3   4  qq_mobile  &nbsp;杀神免费版        游客   1403781770   \n",
       "4   5  qq_mobile  &nbsp;杀神免费版        游客   1404561529   \n",
       "\n",
       "                                             content score  type  \n",
       "0                          修复手机卡进行道具付费的漏洞！功能实用。速度很快。     5     1  \n",
       "1  不错，不错这个游戏非常好玩随然登陆时间比较长但非常好玩的我看了一下登陆时间也差不多是一两分钟...     5     1  \n",
       "2                                   新区，推荐。我叫一杯凉茶。一起玩     5     0  \n",
       "3                                请问通过人物，能不能找到账号和所在区。     5     0  \n",
       "4  经常卡死。经常卡死。经常卡死。经常卡死。响应很慢。响应很慢。响应很慢。响应很慢。响应很慢。响...     1     1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pymysql\n",
    "import pandas as pd\n",
    "import re\n",
    "from bs4 import BeautifulSoup\n",
    "from hanziconv import HanziConv\n",
    "from zhon import hanzi\n",
    "import string\n",
    "punctuation=hanzi.punctuation + string.punctuation\n",
    "punctuation = set([i for i in punctuation])\n",
    "import html\n",
    "conn = pymysql.connect(host='127.0.0.1',port=3306,user='root',password='eXYhzAWjyvy8grwM',db='game_process',charset='utf8mb4')\n",
    "sql = \"SELECT id,source,game_name, user_name,comment_time,content, score,type FROM s_lcs_game_comments_qq WHERE type=2 or type=3\"\n",
    "df_comment = pd.read_sql(sql, conn)  # 返回的是 dataFrame 数据结构\n",
    "df_comment['type'] = df_comment['type'] - 2\n",
    "conn.close()\n",
    "df_comment.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>source</th>\n",
       "      <th>game_name</th>\n",
       "      <th>user_name</th>\n",
       "      <th>comment_time</th>\n",
       "      <th>content</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>402</td>\n",
       "      <td>402</td>\n",
       "      <td>402</td>\n",
       "      <td>402</td>\n",
       "      <td>402</td>\n",
       "      <td>402</td>\n",
       "      <td>402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>742</td>\n",
       "      <td>742</td>\n",
       "      <td>742</td>\n",
       "      <td>742</td>\n",
       "      <td>742</td>\n",
       "      <td>742</td>\n",
       "      <td>742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  source  game_name  user_name  comment_time  content  score\n",
       "type                                                                 \n",
       "0     402     402        402        402           402      402    402\n",
       "1     742     742        742        742           742      742    742"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# type 2 无用评论，3 普通评论 \n",
    "# 统计分析，两个类别的样本分布均匀\n",
    "df_comment.groupby('type').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extract_text(x):\n",
    "    soup = BeautifulSoup(x, 'html.parser')\n",
    "    return soup.text\n",
    "\n",
    "def clear_text_punctuation(x):\n",
    "    x = html.unescape(x)  # 反转义字符,显示真实内容。\n",
    "    x = HanziConv.toSimplified(x) # 汉字繁体 转成 简体\n",
    "    x = x.strip() # 去除字符首尾的指定字符\n",
    "    x = \"\".join([i for i in x if i not in punctuation]) # 遍历评论每个字符，若是标点，去掉用空字符连接\n",
    "    return re.sub(r'\\s+', ' ', x) # 把x通过正则表达式 (\\s 代表空格，+ 代表多个)，替换成空字符   \n",
    "# 数字和字母过滤方法，及一些特殊符号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>source</th>\n",
       "      <th>game_name</th>\n",
       "      <th>user_name</th>\n",
       "      <th>comment_time</th>\n",
       "      <th>content</th>\n",
       "      <th>score</th>\n",
       "      <th>type</th>\n",
       "      <th>clear_content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>qq_mobile</td>\n",
       "      <td>&amp;nbsp;杀神免费版</td>\n",
       "      <td>游客</td>\n",
       "      <td>1381762690</td>\n",
       "      <td>修复手机卡进行道具付费的漏洞！功能实用。速度很快。</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>修复手机卡进行道具付费的漏洞功能实用速度很快</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id     source    game_name user_name comment_time  \\\n",
       "0   1  qq_mobile  &nbsp;杀神免费版        游客   1381762690   \n",
       "\n",
       "                     content score  type           clear_content  \n",
       "0  修复手机卡进行道具付费的漏洞！功能实用。速度很快。     5     1  修复手机卡进行道具付费的漏洞功能实用速度很快  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comment['clear_content'] = df_comment['content'].apply(clear_text_punctuation)\n",
    "df_comment.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import jieba\n",
    "def jieba_word(x):\n",
    "    word_list = [i for i in jieba.cut(x)]\n",
    "    return ' '.join(word_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache /tmp/jieba.cache\n",
      "Loading model cost 0.749 seconds.\n",
      "Prefix dict has been built succesfully.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'我 想 去 吃饭'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jieba_word('我想去吃饭')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>source</th>\n",
       "      <th>game_name</th>\n",
       "      <th>user_name</th>\n",
       "      <th>comment_time</th>\n",
       "      <th>content</th>\n",
       "      <th>score</th>\n",
       "      <th>type</th>\n",
       "      <th>clear_content</th>\n",
       "      <th>jieba_word</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>qq_mobile</td>\n",
       "      <td>&amp;nbsp;杀神免费版</td>\n",
       "      <td>游客</td>\n",
       "      <td>1381762690</td>\n",
       "      <td>修复手机卡进行道具付费的漏洞！功能实用。速度很快。</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>修复手机卡进行道具付费的漏洞功能实用速度很快</td>\n",
       "      <td>修复 手机卡 进行 道具 付费 的 漏洞 功能 实用 速度 很快</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id     source    game_name user_name comment_time  \\\n",
       "0   1  qq_mobile  &nbsp;杀神免费版        游客   1381762690   \n",
       "\n",
       "                     content score  type           clear_content  \\\n",
       "0  修复手机卡进行道具付费的漏洞！功能实用。速度很快。     5     1  修复手机卡进行道具付费的漏洞功能实用速度很快   \n",
       "\n",
       "                         jieba_word  \n",
       "0  修复 手机卡 进行 道具 付费 的 漏洞 功能 实用 速度 很快  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comment['jieba_word'] = df_comment['clear_content'].apply(jieba_word)\n",
    "df_comment.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# # 构成词典 \n",
    "# from gensim import corpora\n",
    "# words = [x for x in df_comment['jieba_word']]\n",
    "# dic = corpora.Dictionary(words)\n",
    "\n",
    "# # 字数 14086， 感觉一千条评论不够，该手动分类一万条\n",
    "# # 参考技术文章是 手动分类了一万条推特 twitter 的评论\n",
    "# all_words = [y for x in words for y in x]\n",
    "# print('%s words total, with a vocabulary size of %s ' % (len(all_words), len(dic)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9JgB7TlUdnuSRSR7Q3ddU1T2yuP7WTXFwkkOTvHVPzTeAhyT5UpK/3+Q5gE1i\nzxmw0e6V5LM7L6jZ3Z/t7n9Okqr6wao6q6rOrqq3r7ldyplV9byqen9V/UNV/eh0547/muQJ0963\nJ1TVnarqldN251TV0dPPP7mq3lhVb6uqT1TVH+8cpqoeVlUfrKoPV9UZ07obfJ31VNVeVfUnVfWB\naW/gL07rHzLNfmpVfbyqXjNdtDJV9Yhp3dlV9ZKqektVbUvyS0l+ffo3/ej0Kx5cVX9fVRfZiwa3\nfvacARvtHUmeXVX/kORvk7y+u8+a7p36Z0mO7u4dVfWEJH+Q5Oenn9vS3Q+cDmM+p7t/vKqeneTQ\n7v6VJKmqP8zi1lE/X1X7JHl/Vf3t9PMHJzkkyTVJLqiqP0vy1ST/PcmDu/ufqmrfadvfvqHX6e4v\nr/NvOjaL27b8UFXdLsnfVdU7pucOSfK9Sf45yd8lOaKqtid5+Zrf+9ok6e6Lq+ovknypu/90+jcd\nm0XQ/kiS78nidjGn3vj/swO3FOIM2FDd/aWq+sEkP5rkx5K8vqpOSLI9yfcleee0c2mvJGvvUbfz\nZvdnJ9m2zsv/uyxuwP6b0/Lts7jdSpKc0d1XJUlVnZfk3knuluQ93f1P02yf383rnL+L3/sDa/Zq\n3TXJQUmuTfL+7r50+r0fmmb/UpKLdv7eJK9Nctw6r50kb+ruryc5r6ruuYvtgFsBcQZsuO7+WpIz\nk5xZVR/N4kbCZyc5t7sPX+fHdt5X8GtZ/79dleQ/dPcF37JyccLB2vsS7uo11n2d3Wz/tO5++/V+\n70Nu5O9dz9rXqJvw88AtiO+cARuqqu5bVQetWXVwFjcSviDJ1umEgVTV3lX1vbt5uauT3GXN8tuT\nPG3N97oO2c3PvzeL73MdOG2/87DmjX2dtyf5T9Oh2VTVd1fVnXax/QVJ7jN9xyxZ3PR6vX8TsGLE\nGbDR7pzklKo6r6o+kuR+Sf5Ld1+b5LFJnldVH07yoSQP2sXrJMm7k9xv5wkBSZ6bZO8kH6mqc6fl\ndXX3jiwOJ75x+p2vn566Ua+T5BVJzkvywenyGi/PLvaQdff/S/LLSd5WVWdnEWRXTU//TZJ/f70T\nAoAVUt292TMArJyquvP0/btK8tIkn+juF272XMDms+cMYHP8wnSCwLlZnEDw8k2eBxiEPWcAAAOx\n5wwAYCDiDABgIOIMAGAg4gwAYCDiDABgIOIMAGAg/x/OooEwbA4W1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "sentence_lengths = [len(x) for x in df_comment['clear_content']]\n",
    "\n",
    "fig = plt.figure(figsize=(10, 10)) \n",
    "plt.xlabel('Sentence length')\n",
    "plt.ylabel('Number of sentences')\n",
    "plt.hist(sentence_lengths)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "def cv(data):\n",
    "    count_vectorizer = CountVectorizer()\n",
    "    # emb 是词频矩阵呀,感觉和gensim 一样的效果\n",
    "    # fit_transform 先获取词汇词典，返回词汇矩阵\n",
    "    # transforn 返回词汇矩阵， 获取词典是以空格切分\n",
    "    emb = count_vectorizer.fit_transform(data)\n",
    "    return emb, count_vectorizer\n",
    "\n",
    "list_corpus = df_comment[\"jieba_word\"].tolist()\n",
    "list_labels = df_comment[\"type\"].tolist()\n",
    "# X_test  len = len(list_corpus) * test_size \n",
    "X_train, X_test, y_train, y_test = train_test_split(list_corpus, list_labels, test_size=0.2, \n",
    "                                                                                random_state=40)\n",
    "X_train_counts, count_vectorizer = cv(X_train)\n",
    "X_test_counts = count_vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(915, 1511)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for x,y in count_vectorizer.vocabulary_.items():\n",
    "#     print(x,y)\n",
    "X_train_counts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# # data1 = [' is good content', 'lcss is bad good ']\n",
    "# data1 = ['修复手机卡进行道具付费的漏洞！功能实用。速度很快。', '功能实用。速度很快']\n",
    "# lcs = CountVectorizer()\n",
    "# # emb 是词频矩阵呀,感觉和gensim 一样的效果\n",
    "# # fit_transform 先获取词汇词典，返回词汇矩阵\n",
    "# # transforn 返回词汇矩阵\n",
    "# lcs1 = lcs.fit_transform(data1)\n",
    "# for x,y in lcs.vocabulary_.items():\n",
    "#     print(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min x:-2.60536287909e-14, max x:6.98990174997\n",
      "min y:-0.119308756709, max y:32.4880010439\n"
     ]
    },
    {
     "data": {
      "image/png": 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AACIijAEAAEREGAMAAIiIMAYAABARYQwAACAiwhgAAEBEhDEAAICIaiSMmdnxZva5mc0x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\nAACIiDAGAAAQEWEMAAAgov8HJ9yAyGu6VVUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA, TruncatedSVD\n",
    "import matplotlib\n",
    "import matplotlib.patches as mpatches\n",
    "import math\n",
    "def plot_LSA(test_data, test_labels, savepath=\"PCA_demo.csv\", plot=True):\n",
    "        # PCA 降维\n",
    "        lsa = TruncatedSVD(n_components=2)\n",
    "        lsa.fit(test_data)\n",
    "        lsa_scores = lsa.transform(test_data)\n",
    "        # 查看数据降维效果\n",
    "        #print(lsa_scores[1:100,:]) \n",
    "        print('min x:%s, max x:%s' % (min(lsa_scores[:,0]),max(lsa_scores[:,0])))\n",
    "        print('min y:%s, max y:%s' % (min(lsa_scores[:,1]),max(lsa_scores[:,1])))   \n",
    "        # type 2 useless, 3 common  结果 color_mapper={2:0, 3:1}\n",
    "        color_mapper = {label:idx for idx,label in enumerate(set(test_labels))}\n",
    "        color_column = [color_mapper[label] for label in test_labels]\n",
    "        colors = ['orange','blue','blue']\n",
    "        if plot:\n",
    "            plt.scatter(lsa_scores[:,0]*math.pow(10,2), lsa_scores[:,1]*math.pow(10,0), s=8, alpha=.8, c=test_labels, cmap=matplotlib.colors.ListedColormap(colors))\n",
    "            red_patch = mpatches.Patch(color='orange', label='useless')\n",
    "            green_patch = mpatches.Patch(color='blue', label='common')\n",
    "            plt.legend(handles=[red_patch, green_patch], prop={'size': 10})\n",
    "# 怎样才能让 数据点 出现在屏幕上\n",
    "fig = plt.figure(figsize=(10, 10))          \n",
    "plot_LSA(X_train_counts, y_train)\n",
    "# 调节图像x,y 的大小，自己根据x y 轴慢慢调节\n",
    "plt.axis([0,1,-2,2]) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0,\n",
       "       0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0,\n",
       "       1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1,\n",
       "       1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "# 逻辑回归 分类器\n",
    "clf = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg', \n",
    "                         multi_class='multinomial', n_jobs=-1, random_state=40)\n",
    "clf.fit(X_train_counts, y_train)\n",
    "y_predicted_counts = clf.predict(X_test_counts)\n",
    "y_predicted_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 0.886, precision = 0.888, recall = 0.886, f1 = 0.887\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report\n",
    "\n",
    "def get_metrics(y_test, y_predicted):  \n",
    "    # true positives / (true positives+false positives)\n",
    "    precision = precision_score(y_test, y_predicted, pos_label=None,\n",
    "                                    average='weighted')             \n",
    "    # true positives / (true positives + false negatives)\n",
    "    recall = recall_score(y_test, y_predicted, pos_label=None,\n",
    "                              average='weighted')\n",
    "    \n",
    "    # harmonic mean of precision and recall\n",
    "    f1 = f1_score(y_test, y_predicted, pos_label=None, average='weighted')\n",
    "    \n",
    "    # true positives + true negatives/ total\n",
    "    accuracy = accuracy_score(y_test, y_predicted)\n",
    "    return accuracy, precision, recall, f1\n",
    "\n",
    "accuracy, precision, recall, f1 = get_metrics(y_test, y_predicted_counts)\n",
    "print(\"accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f\" % (accuracy, precision, recall, f1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import itertools\n",
    "from sklearn.metrics import confusion_matrix\n",
    "# 检验模型情况，是漏判，还是误判严重\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.winter):\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title, fontsize=30)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, fontsize=20)\n",
    "    plt.yticks(tick_marks, classes, fontsize=20)\n",
    "    \n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt), horizontalalignment=\"center\", \n",
    "                 color=\"white\" if cm[i, j] < thresh else \"black\", fontsize=30)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label', fontsize=10)\n",
    "    plt.xlabel('Predicted label', fontsize=10)\n",
    "\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cc4BDgDuB84CXgbWAXYB9gT+1HBgR+wK/AJYBfgM8AKwDvBd4Z0TskZm3dHXC\niDgKOB14Ffg1MAPYFPgI8E8R8ZbMfLQ+dk3gZmAc8DvgUmA5YEPgw8APgZYu/k2/Fg1Pf37geU69\n7ik+sfMb2HS1Mdz5xYmcccPT1ZXMRga7bTSOwyavtih7e/rfnuYP987pdMyDJo5nxeVGAnDVA3N5\n9Lkle+tK6jsbbABHH734Y296U+v2nnvCqDZ/XS+9FG67bfHHVloJvvCFxR9bf/3W7e22g298Y/H9\nV10Ff/5zj6YtLVWGdYAbESsBBwPTgB0zc0Gb/Y3Z0VWAi4B5wG6ZeXfDvq2BG4CfApO6OOdmwI+B\n6cDumfl4w769gMuBU4D31A+/D1gV+ExmntJmrOWBhd19LRrePvWLh1mwMPnkzm9glbGj+NKea7d7\n3PeveZLP/mp6l+M1Xpr3rJssT5D62/rrw9e+1vH+3Xarbo0eeGDJAHfllTsfZ+LE6tZo/nwDXKkZ\nwzrApVorGFSZ1CWKFDOz8fqmhwErA59qDG7r4+6MiDOAz0TEVm33t/HPVBngf20MbutxroyIX1Nl\ncVfMzMZO/i/TRmY2NkPtzmtZTEQcAxwDwIprdDJ1DQWZ8OlfTueCW2Zx9I6rM2Xjcaw1ripVeHzu\na/zloec57boqq9uVjScsx24bjwPguXnz+cXtzV3SV5JUoMQuCrVhHeBm5vMR8Rvgn4DbIuJS4Brg\nxsxse43SneqfEzuo792s/rkl0FmA2zLO7hHx5nb2rw6MrMebRlXC8B/AjyJiH+CPwHXA3Zm56New\nm69lMZl5OlXJBLHGlv5qDxM3PvIiNz7yYq/GeHDWK8Tn/tZHM5LUjL/8BaIPSgwfeaRvxpG0pGEd\n4NY+AHwZ+CBwQv3YKxFxCfCFzHy6fqzlK/6PdjHeCl3sbxnni82Mk5mPRMQOwPFUdbTvrffPiIjv\nZOb3G57T7GuRJEla0hBY4DUUDLkuCrR+Pd9R8L1y453MfDkzj8/MzYD1gEOBa+uflzQc2tIYdGJm\nRie3c7uYX8s4K3Uxzl8a5nhPZn6AKjieDHyF6r0/JSKObjiu2dciSZKkDgzFALelG/66bXdExCbA\nSh09MTNnZOYFwD5UHRJ2aVicdUP9c9dezq/H42Tm/MyclpnfouqWAHBAB8d29lokSZLUgaEY4N4L\nPA/sHxGLlodHxBig8et8ImK1iNimnTGWpyoRmA+09Ew6G5gDHFeXDCwmIkZExJQm5vdD4HXg5Lqj\nQttxRkfErg33t687JLTVshpsXg9eiyRJ0pJyiNwG2ZCrwc3M1yPiFODfgVsj4pdU83wb8ER9a7F2\nfcwdwO1U/WjHAe8C3gB8v6WQM14vAAAgAElEQVSTQWY+GxHvA34J3BARVwJ3Uf1nWJdq8dh4qh61\nnc3v3roP7lnAXRHxB+A+qs4K61FldmcCW9RP+TDwsYi4FniQKkO9MdVisleB/+7ua5EkSVLHhlyA\nWzuOKrP5Uar2V08BP6NaqNXY4WB6fewUYA9gAjAb+AdVnevPGget23i9CfgC1Vf/u1JlRZ8ArqK6\nCEOXMvN/IuLvwOfr874deKke5xLg5w2HXwQsC7wV2B4YAzxez+27mXlnT16LJEmS2hcNnapUgFhj\ny+RDZw32NCT1xsk7dX2MpCFsMplTB7ydQWy8cXLStwb6tO37wEHTMnPyYJ1+KNbgSpIkST02VEsU\nJEmS1F1+MQ+YwZUkSVJhDHAlSZJUFEsUJEmSSpB4qd6aGVxJkiQVxQBXkiRJRbFEQZIkqRR2UQDM\n4EqSJKkwZnAlSZKKEC4yq5nBlSRJUlEMcCVJklQUSxQkSZJK4SIzwAyuJEmSCmOAK0mSpKJYoiBJ\nklQKuygAZnAlSZJUGANcSZIkFcUSBUmSpBIkdlGomcGVJElSUczgSpIklcJFZoAZXEmSJBXGAFeS\nJElFsURBkiSpFC4yA8zgSpIkqTAGuJIkSSqKJQqSJEmlsIsCYAZXkiRJhTGDK0mSVAKvZLaIGVxJ\nkiQVxQBXkiRJRbFEQZIkqRQuMgPM4EqSJKkwBriSJEkqiiUKkiRJpbCLAmAGV5IkSYUxgytJklSE\ncJFZzQyuJEmSimKAK0mSpKJYoiBJklQKF5kBZnAlSZJUGANcSZIkFcUSBUmSpBIkdlGomcGVJElS\nUQxwJUmSVBRLFCRJkkphFwXADK4kSZIKYwZXkiSpFGZwATO4kiRJKowBriRJkopiiYIkSVIp7IML\nmMGVJElSYQxwJUmSVBRLFCRJkkphFwXADK4kSZIKYwZXkiSpBImLzGpmcCVJklQUA1xJkiQVxRIF\nSZKkUliiAJjBlSRJUmEMcCVJklQUSxQkSZJKYR9cwAyuJEmSCmMGV5IkqQjhIrOaGVxJkiQVxQBX\nkiRJRbFEQZIkqRQuMgPM4EqSJKkwBriSJEkqiiUKkiRJJUjsolAzgytJkqSiGOBKkiSpKAa4kiRJ\npcghcutCRHw2Iu6KiDsj4qKIWC4iNoyIGyPigYj4eUSM7unbYIArSZKkARMRawOfBiZn5tbASOBg\n4FvAyZm5CfAccHRPz2GAK0mSVIqMoXHr2ihgTESMAsYCTwJ7ApfU+88FDujp22CAK0mSpAGTmY8D\n3wEepQps5wLTgDmZOb8+7DFg7Z6ewwBXkiRJfW1CRExtuB3TsiMiVgH2BzYE1gKWB/bty5PbB1eS\nJKkUQ+dSvbMyc3IH+/YGHs7MmQAR8QtgZ2DliBhVZ3HXAR7v6cnN4EqSJGkgPQq8JSLGRkQAewF3\nA38G3lcfczhwWU9PYIArSZKkAZOZN1ItJrsFuIMqHj0d+DLwuYh4ABgPnNnTc1iiIEmSVIphcqne\nzDwOOK7Nww8BO/TF+GZwJUmSVBQzuJIkSSVo8ipiSwMzuJIkSSqKAa4kSZKKYomCJElSKYbJIrP+\nZgZXkiRJRTHAlSRJUlE6LFGIiHGdPTEzn+/76UiSJKnH7KIAdF6DexfV29RYzNFyP4H1+nFekiRJ\nUo90GOBm5roDORFJkiT1RrjIrNZUDW5EHBwRX62314mI7ft3WpIkSVLPdBngRsQPgT2AD9cPzQN+\n3J+TkiRJknqqmT64b83MSRFxK0Bmzo6I0f08L0mSJHWXi8yA5koUXo+IEdRvWUSMBxb266wkSZKk\nHmomwP0RcCmwWkScAFwLfKtfZyVJkiT1UJclCpl5XkRMA/auHzooM+/s32lJkiSpWxK7KNSaqcEF\nGAm8TvXWefUzSZIkDVnNdFH4N+AiYC1gHeDCiDi2vycmSZIk9UQzGdzDgO0ycx5ARJwI3Aqc1J8T\nkyRJUjfZRQFortzgSRYPhEfVj0mSJElDTocZ3Ig4mepzwGzgroj4Y33/7cDNAzM9SZIkNc1FZkDn\nJQotnRLuAv6v4fEb+m86kiRJUu90GOBm5pkDORFJkiSpL3S5yCwiNgZOBLYClmt5PDM368d5SZIk\nqbtcZAY0t8jsHOBsIID9gIuBn/fjnCRJkqQeaybAHZuZfwTIzAcz82tUga4kSZI05DTTB/fViBgB\nPBgRHwceB1bs32lJkiSp2+yiADQX4H4WWB74NFUt7krAUf05KUmSJKmnugxwM/PGevMF4MP9Ox1J\nkiT1SOIis1pnF3r4JZ28TZn53n6ZkSRJktQLnWVwfzhgs1DfmTkWTnvzYM9CUm/k8YM9A0m9MfmJ\nwZ7BUq+zCz1cOZATkSRJUi+5yAxork2YJEmSNGwY4EqSJKkozbQJAyAils3MV/tzMpIkSeoFuygA\nTWRwI2KHiLgDuL++PzEiftDvM5MkSZJ6oJkM7veBdwG/AsjMv0fEHv06K0mSJHVTuMis1kwN7ojM\nfKTNYwv6YzKSJElSbzWTwZ0RETsAGREjgX8B7uvfaUmSJEk900yA+89UZQrrAU8Df6ofkyRJ0lDi\nIjOgiQA3M58BDh6AuUiSJEm91mWAGxFn0M7ngcw8pl9mJEmSJPVCMyUKf2rYXg54DzCjf6YjSZKk\nHknsolBrpkTh5433I+J84Np+m5EkSZLUCz25VO+GwBp9PRFJkiSpLzRTg/scrTW4I4DZwFf6c1KS\nJEnqAbsoAF0EuBERwETg8fqhhZnpWydJkqQhq9MANzMzIn6XmVsP1IQkSZLUQy4yA5qrwb0tIrbr\n95lIkiRJfaDDDG5EjMrM+cB2wM0R8SDwEhBUyd1JAzRHSZIkqWmdlSjcBEwC3j1Ac5EkSVJvuFIK\n6DzADYDMfHCA5iJJkiT1WmcB7moR8bmOdmbm9/phPpIkSVKvdBbgjgRWoM7kSpIkaYizRAHoPMB9\nMjO/PmAzkSRJkvpAlzW4kiRJGgYS++DWOuuDu9eAzUKSJEnqIx0GuJk5eyAnIkmSJPWFTi/VK0mS\npGHEEgWguUv1SpIkScOGAa4kSZKKYomCJElSKeyDC5jBlSRJUmEMcCVJklQUSxQkSZKKEHZRqJnB\nlSRJUlHM4EqSJJXCRWaAGVxJkiQVxgBXkiRJRbFEQZIkqQSJi8xqZnAlSZJUFANcSZIkFcUSBUmS\npFLYRQEwgytJkqTCmMGVJEkqhYvMADO4kiRJKowBriRJkopiiYIkSVIpXGQGmMGVJElSYQxwJUmS\nVBRLFCRJkkphFwXADK4kSZIKYwZXkiSpBImLzGpmcCVJklQUA1xJkiQVxRIFSZKkUrjIDDCDK0mS\npMIY4EqSJKkolihIkiSVwi4KgBlcSZIkFcYAV5IkSUWxREGSJKkIYReFmhlcSZIkFcUMriRJUilc\nZAaYwZUkSVJhDHAlSZJUFEsUJEmSSpC4yKxmBleSJElFMcCVJElSUSxRkCRJKoVdFAAzuJIkSSqM\nGVxJkqRSuMgMMIMrSZKkARYRK0fEJRFxb0TcExE7RcSqEXFFRNxf/1ylp+Mb4EqSJGmgnQL8ITO3\nACYC9wBfAa7MzE2BK+v7PWKAK0mSVIocIrdORMRKwG7AmQCZ+VpmzgH2B86tDzsXOKCnb4MBriRJ\nkvrahIiY2nA7pmHfhsBM4OyIuDUifhoRywNrZOaT9TFPAWv09OQuMpMkSVJfm5WZkzvYNwqYBPxL\nZt4YEafQphwhMzMietz0zAyuJElSKTKGxq1zjwGPZeaN9f1LqALepyNiTYD65zM9fRsMcCVJkjRg\nMvMpYEZEbF4/tBdwN/Br4PD6scOBy3p6DksUJEmSStDEAq8h5F+ACyJiNPAQcCRV4vXiiDgaeAR4\nf08HN8CVJEnSgMrM24D2anT36ovxLVGQJElSUczgSpIklcJL9QJmcCVJklQYA1xJkiQVxRIFSZKk\nUgyfLgr9ygyuJEmSimKAK0mSpKJYoiBJklSEpi6Tu1QwgytJkqSimMGVJEkqhYvMADO4kiRJKowB\nriRJkopiiYIkSVIJEheZ1czgSpIkqSgGuJIkSSqKJQqSJEmlsIsCYAZXkiRJhTGDK0mSVAoXmQFm\ncCVJklQYM7jSABsxArbcAiZPCrafFEyeFEzcBsaOrT51H//NhZxw4sIuxzn79BEc8eHmP6PGmPk9\nnrO01FiwEO6ZBVOfgGlPwNQn4e9Pwcv1v5/jdofjp3Q9zi1Pwg2PVePc8QzMfAlmzYP5C2GVMbDV\navC2jeCIbeENK3Rvjg/OhvNvh98/AI/MgedegVXHwJorwI5rw54bwnu3hJHmsLT0MsCVBtjFF4zg\nwAP8wyMNSe+/BH5xT+/HeccF8PRL7e976sXqdtXDcOI18N23wzHbdz3mgoVw/NXwX9fDawvaH/PW\np+DH0+C5L8PKy/X6ZWgYcpEZYIArDbiRIxe//+yzybOzYbNNe143dcwnF/DMzF5OTFIVRDZadQyM\nHwP3z+7+WBPGwlvWgYlrwIYrw0rLVYHpA7PhV/dWweiLr8HHfgujRsBR23U81vyFcOgv4Od3VfdX\nGwsHbgmT1qwywi++Bvc/C396GG5+vPtzlQpjgCsNsJtuTu65dyHTbk2m3ZJMfwQOPzQ454yRXT+5\nA5f/KXnk0T6cpLS02mFt2HICbL8WbL8mbLgKnHMbHHlZ98a58rCqDCE6+OD6/3aHk66Br15V3f/8\n5fChbWDZDv4sH391a3B75Lbw/f1ghdFLHnci8OQL7e+TliIGuNIAO+nbid8hSUPUV3ftm3HeuHrX\nxxy7K/zsLrj9aZjzClw3o6qfbevOZ+Bb11XbB2wBZ+3f+bhrrtj9+aocdlEA7KIgSdLg2Wq11u2n\nXmz/mJP/VpUoBHDyPgMyLWm4M4MrSdJgebChtre9bgovv15leQF2WQ82WHlg5qXhyS8IFzHAlQpw\nxqkj2HyzYI3V4eWX4Ykn4bq/JedfuJBrrhvs2Ulq14+nws1PVNtrLA87r7vkMdOehHmvV9s7rF39\nvOphOPVm+NtjVeuxVZaDiW+Ag7aCwyfCMj2v55dKYYArFeBte7VWGy27LKy8Mmy1ZfDRo0bw298t\n5LCPLOS55wZxgtLS7K+PwOyXq+1X58P0OfDb++HaemXomFFw9v7tLzCb+kTr9jrj4F9+Bz+8efFj\nnn4JLn+wuv33DfCbQ6rFcdJSzABXGsaefz654qrkpqnJjMdgwQJYZ214+17BPm+rgt53vWMEf7k8\n2HnPBbzwwiBPWFoafekKuLGd1l0jA/beCE7aC7Zbs/3nNtblnjYV7nu2et4h28CeG8Byo6oLSZxx\nS5XNvWsm7HEu3Pqxqn2Ylj6WKAAGuNKw9YPTFvLJz8C8eUvu+94pyS47L+SSC0ayxhrBNlsH3/3P\nERzzya6vkCZpgKy/Mrx9Y1hvpY6PmfNK6/Z9z1YB7e8/BFM2aH38EOCzb4G9zquC3UfmwlevhNPe\n1V8zl4Y8uyhIw9Qtt7Yf3La49jp47yELWLiw+jh/5GHBWmsN0OQktbrhI5DHVbcXj4Vpx8BXdq76\n1X7+ctj2J9VlgduzsE067mu7Lh7ctlhtebjgvVWnBYCzb4PnX+3LVyENKwa4UsGu/1t1EQiAUaOC\nffa2P6I0qJYfXV197KS94bqjYMXR8NjzsPf58EQ7NUQrtrlgw0c7uaTvNmtUV04DeHUBXOfVX5ZK\nGUPjNsgMcKXCXX1NawZoi80H/386kmrbrQlf2rnanvMKnHLDksesvFzr9rrjYPXlOx9z+4Za3gdd\nWaqllwGuVLhnn23dXrmTUj9Jg2DfTVq3r35kyf2bT2jdXmm5Jfe31XjM3Fc6Pk4qnIvMpMKNH9+6\nPWfu4M1DUjsaSxDmtBOQvmmN1u1mAtbGY5oJiFWYoVEeMBSYwZUKt/surf+zu+9++8dIQ8oDDVcy\nmzB2yf1bTIAN66uXzXgennmp8/GmPdm6vdn4jo+TCmeAKxXsrTvBPm+rAtwFC5I//skAVxpSTr+l\ndfut67R/zMFbt26fMa3jse54Gm54rNpeYXR1aV8tfXKI3AaZAa40DH34g8Hee3b+NdTOb4VfXDSS\nESOq4867IHnssYGYnbSUO+e26qpi2clf+dcWwBcuh1//o7o/eiR8ZFL7x35+p9bFZt+8Bq6evuQx\nM1+CD/2iNbD4xGQYu0xPX4E07PV7DW5E7AB8HtgFmADMBu4AfpqZFzcc937gU8BEYDTwAHAh8L3M\nfLXNmNPrza2BbwDvq8f+B3B8Zv4qIkYBXwaOANYFHgdOzswfthlrCvBn4ATgt8A3gZ2AhcBVwGcy\nc0ZEbAT8B7AXsAJwQ73v7+285jWBrwHvBNYC5gLXACdm5rQ2xx4BnA0cCTwCHAdsT/W/qWuAL2Tm\nPe2/uxqONlgfjj5i8c+Wb2pI0Ow5JRg1avH9l/5qIbc1/KZN2i74zKdG8OiM5I9XJHfclcyctfiV\nzN6+dywKbu+8K/nsl7zIg9Slh5+DM29d/LHbn27dvuphmN/m39KBWy5+JbLbnoIjL6surfu2jao6\n2tWXr4LY2S9X4/3y3sXbgn3nbYsvKGs0fiyc9k744KXwynzY+zz44Daw54b1lcyerq5kNrNujD1x\nDTh+So/fAqkE/RrgRsRHgdOABcCvgfuB1YHJwCeAi+vj/gM4FphFFdS+COxHFVDuExFvz8zX2gy/\nDHAFsCpwGVVQfAhwaUS8vR5/R+D3wKvAQcAPImJmZv68nem+mSog/gtwBrAN8F5g64jYH7gWuBc4\nD1i/3ndFRGyUmYuupRgRG9bHrkUVIF9EFWAfBLwzIg7MzN+2c/53AfvX8/0xsBXwDuDNEbFVZs7q\n4G3WMLP+esHXvtLxlye77RLstsvi2dkHHkxu+/uS2aD11g0+elTnmdxfXLaQj35iIXNdYCZ17ZG5\ncOI1He+/5tHq1miTVdu/1O5jz1cXXOjM6svDKfsuXobQnoO3roLbT/0OXnodzr+9urW1+/pwyfth\njNnbpVLiIrNavwW4EbEVcCrwPLBrZt7VZv869c+dqILbGcAOmflU/fixwC+pAr8vUAW7jdYCbgGm\ntGR4I+J84K/A/wIPAltn5px63/eoAtSvAO0FuO8ADs3MCxrmeCZwFHA98N3MPLFh378DXweOBk5p\nGOfH9dy+1ub4U+u5nRsR6zcGxbUDgH0y88qG55xUz/co4L/amXPLcccAx1T3rLlaGnz75IVMnZbs\ntGMwabtgjdVhwnhYbjmYOxcefgSuvyE574KF3NrF31dJfezEPWGvDatSglueqhaSzZoHry+oamPX\nXBG2fQPst0mV/V1+dJdDAnDEtrDHBvCTafB/98Ojc2He67DaWNhxHTh0GzhgCwgDHCmysxqh3gwc\n8QOqkoPPZebJnRx3BvAR4GOZeXqbfZsB9wCPZOZGDY9Pp8qibpKZD7Z5zkPAhsBemXlVm31/piqV\nWC4zF9SPTaEqUbg2M3dtc/xuVBnd6fW5FjTsW79+/JzMPLJ+bB2qQP3R+vjX24x3PnAocHhmnlc/\ndgRVicIFmXlom+M3BB4CLs3M97X7BrYRI7ZPlr2xmUMlDVUvf3OwZyCpNyafTk59YsA/acQqWyd7\n/e9An7Z9l241LTMnD9bp+3OR2Vvqn7/v4riWqvqr2u7IzPuAx4ANI6Jti/o5bYPbWssFvdtbavo4\nVdb6De3sm9rJWLc1BrcNYwE0Lnvdrv55TdvgtnZVm+O6Ov+M+ucq7eyTJEla3GB3T1gKuijUjfsW\nBYIdaQlcn+xgf8vjK7d5vKOKwvkAmdne/vn1z/aKkzo7fol9mdneWD19LQBzOjnHyA7GkyRJUhv9\nGeC2BGxrd3FcS/DYXlYVYM02xw1lJb0WSZI03GQMjdsg688A94b6535dHNfSj2VK2x0RsQlVCcDD\nLYvFhriW17JL3aasrT3qn7e0s0+SJEl9oD8D3NOovuL/97qjwmJauigAZ9U/vxYRqzXsHwl8p57j\nmf04zz6TmY9RtS7bAPhM476I2BH4IPAcVXcISZIk9YN+axOWmXdHxCeo2mbdGhGXUfXBHU/Vc/Z5\nYI/MvD4i/gv4EnBnRFwCvESV+d2aqqfst/trnv3g48B1wLfrfrxTae2DuxA4MjNf6OT5kiRJPTME\nFngNBf16oYfMPCMi7qTqYzuFqtfrLOB24KcNx305Im6lait2GNXCrQeprgb23XYu8jBkZeZDETGZ\nau7voHrdzwN/oLqS2c2DOD1JkqTi9VsfXA0O++BKBbAPrjS8DWYf3CmXDPRp2/erLQe1D26/ZnAl\nSZI0QLxU7yL9uchMkiRJGnBmcCVJkkph5SlgBleSJEmFMcCVJElSUSxRkCRJKoWLzAAzuJIkSSqM\nAa4kSZKKYomCJElSKeyiAJjBlSRJUmEMcCVJklQUSxQkSZKKEHZRqJnBlSRJUlHM4EqSJJUgcZFZ\nzQyuJEmSimKAK0mSpKJYoiBJklQKF5kBZnAlSZJUGANcSZIkFcUSBUmSpFLYRQEwgytJkqTCmMGV\nJEkqhYvMADO4kiRJKowBriRJkopiiYIkSVIpXGQGmMGVJElSYQxwJUmSVBRLFCRJkkqQ2EWhZgZX\nkiRJRTGDK0mSVAoXmQFmcCVJklQYA1xJkiQVxRIFSZKkUrjIDDCDK0mSpMIY4EqSJKkolihIkiSV\nwi4KgBlcSZIkFcYAV5IkSUWxREGSJKkIYReFmhlcSZIkFcUMriRJUgkSF5nVzOBKkiSpKAa4kiRJ\nKoolCpIkSaVwkRlgBleSJEmFMcCVJElSUSxRkCRJKoVdFAAzuJIkSSqMGVxJkqRSuMgMMIMrSZKk\nwhjgSpIkqSiWKEiSJJXCRWaAGVxJkiQNgogYGRG3RsRv6/sbRsSNEfFARPw8Ikb3dGwDXEmSJA2G\nfwXuabj/LeDkzNwEeA44uqcDG+BKkiSVIKm6KAyFWxciYh3gncBP6/sB7AlcUh9yLnBAT98KA1xJ\nkiT1tQkRMbXhdkyb/f8NfAlYWN8fD8zJzPn1/ceAtXt6cheZSZIklWLoLDKblZmT29sREe8CnsnM\naRExpT9OboArSZKkgbQz8O6IeAewHDCO/9/evYfrPtZ5HH9/EmUQSs4Klcoo50NnpoZShmk6TipD\nKk0zuToMJq4008GkqdF0kNKk6aCiLqopGRKKBhs5RuSQKEoIHfCdP373yuNp7b32XjbP2rf367rW\n9Vvrd9/Pff9+6/LzfPd3fe/7gUOAlZI8uGVx1waume0ElihIkiTpflNV+1XV2lW1LvAy4MSqegXw\nHeBFrdurgWNmO4cBriRJUi8mvbhsIReZzcc+wJuT/JihJvfw2Q5kiYIkSZImoqpOAk5q318ObLU4\nxjWDK0mSpK6YwZUkSerF3NlFYaLM4EqSJKkrBriSJEnqiiUKkiRJvbBEATCDK0mSpM6YwZUkSerB\nvduDtitmcCVJktQVA1xJkiR1xRIFSZKkXliiAJjBlSRJUmcMcCVJktQVSxQkSZJ64T64gBlcSZIk\ndcYMriRJUi9cZAaYwZUkSVJnDHAlSZLUFUsUJEmSeuEiM8AMriRJkjpjgCtJkqSuWKIgSZLUg8Jd\nFBozuJIkSeqKGVxJkqReuMgMMIMrSZKkzhjgSpIkqSuWKEiSJPXCRWaAGVxJkiR1xgBXkiRJXbFE\nQZIkqRfuogCYwZUkSVJnDHAlSZLUFUsUJEmSuhB3UWjM4EqSJKkrZnAlSZJ6ULjIrDGDK0mSpK4Y\n4EqSJKkrlihIkiT1wkVmgBlcSZIkdcYAV5IkSV2xREGSJKkX7qIAmMGVJElSZ8zgSpIk9cJFZoAZ\nXEmSJHXGAFeSJEldsURBkiSpFy4yA8zgSpIkqTMGuJIkSeqKJQqSJEk9KNxFoTGDK0mSpK6YwZUk\nSeqFi8wAA9z+1Lwb+O3SV076MnSfWQW4YdIXofuYf2Hsnc9x/x496Qt4oDPA7UxVPXLS16D7TpIz\nq2qLSV+HpNnzOZbuewa4kiRJvXCRGeAiM0mSJHXGAFdashw26QuQdK/5HEv3MUsUpCVIVfnGKC3h\nfI51n3IXBcAMriRJkjpjgCtJkqSuGOBKS5Akn05SSdad9LVIkuaaDLsozIWvCTPAlSRJUldcZCZJ\nktSDwkVmjRlcSZIkdcUAV5pBkm1b3euB82m/IskVIz8vk+Qfk8xLcmOS21qfY5I8Z5rXP6HV1l6d\n5PdJfp7k80kev4jXuXWSo5Jc18a5OsnHk6w5Td/1kxyW5MdJbk/yqyTnJTk0ySNmey/SpCXZKskX\nk1yT5HdJrk3y7SQvGev3kiQnJ7mpPQPnJdkvyUOmGfOK9rV8kg+2Z+v2JOck2aX1eXCStye5NMlv\nk1yW5I3TjPXH/58k2SLJt9o13Jjk6CTrtH7rJzkyyfVtru8k2Xg+97xGko+0a/x9e81Xkmw+Td/d\n2vy7JdkuyUlJbklyc5JvJHnibH/30lxiiYK0+H0aeDlwPvAZ4HZgTeDpwHOB/53qmOS5wFeApYGv\nAT8G1gZeCDw/yXZVNW+mCZPszrB5/O+AY4GrgccBrwF2SrJNVV3V+q4BnAE8DPgf4GjgocB6wCuB\nDwO/XNR7kSYtyZ7Ax4A7GZ6DS4FVgS2ANwBfav3eA+wH3AB8HvgN8DzgPcAOSbavqt+PDb80cDzw\ncOAYYBmGZ+PoJNu38bcGvsnwHL4Y+M8k11fVF6e53C2BfYDvAp8AnsTw3G+UZGfgVOBihufu0a3t\n+CTrV9VvRu55vdZ3TeBE4AvAOm3+5yf5m6r6+jTzvwDYuV3vocCGwI7Alkk2rKob5vNr1lw3BxZ4\nzQUGuNJilGRF4GXAWcDWVXXnWPtodnRlhjej24BnVtWFI20bAacDnwQ2m2HODRjeoK4AnlVV14y0\nPRv4NnAI8Nft9IsY3qT3rqpDxsZaDrhrUe9FmrQkGwIfBW4GnlFVF4y1r92OT2EIbq8Gtqqq69r5\n/YCvMgR+b2UIdketCQiVF1IAAAv7SURBVMwDtq2q37XX/DdwMvBl4DJgo6r6dWv7AEOAui8wXYC7\nI7BrVX1u5BoPB3YHvg/8e1W9e6TtAOBfgD0Ynucph7Zr23+s/0fbtR2R5NGjQXGzC7BDVZ0w8pr3\ntuvdHXjfNNcsLTEsUZAWrwLCkMG5608aq3458uOrgJWAd4wGt63f+QxZnU3bG/eC7MWQXXrTaHDb\nxjmBIZO1U5IVxl53+zTXd2tVTZ1flHuRJm0vhqTNv44HtwBV9dP27e7t+K6p4La13wG8heG/9dfM\nZ469p4Lb9ppTgJ8AKwP7TAW3re1y4HsMGdmlphnr1NHgtjmiHW8CDhpr+0w7bjJ1ogXt2wNXMRaQ\nVtX3Gf4B/XCG7O+4I0eD22bqE9a2mqa/tEQxgystRlV1c5KvATsB5yQ5GjgF+EFV3TbW/SntuHGm\nr+/doB2fCFw4Tfv4OM9KsuU07asCS7XxzmIIeN8DfCTJDsBxDG/EF1bVH9ffLuK9SJO2TTt+c4Z+\nU38ROXG8oaouSfJTYL0kK1bVTSPNv66qy6YZ72cM5T1nTdN2DcP77Ort+1FnzmcsgHPG/2Iy8vq1\nR85t2o6nVNUfphnvRGDX1u8zY23TzX91O648TZuWFO6iABjgSveFlzLU1v0t8M527rdJjgLeWlU/\nb+em/sS/5wzjLT9D+9Q4b1uYcarqyiRbAQcy1NFOZXeuTvL+qvrQyGsW9l6kSVupHccDyXErtuO1\n82m/FnhUG280wL1p+u7cATAWDN+jjeEvLOMW1P9P2qrqjiTjYy3MvcDdv5tRvx4/MTLHdBlnaYli\niYI0s6k/z8/vH4T3ePOoqtur6sCq2oDhjXJXhkUguwJHjXSdehPbuKqygK8jWLCpcVacYZzvjlzj\nRVX1UobgeAuGursHAYck2WMW9yJN2lTAttYM/aael9Xn077GWL+5rKd70eIy6U8w85PMpCXGje24\nznhDksdydxblT1TV1a3ObgeGHRKePrI46/R2fMa9vL5Zj1NVd1TVWVX1bwwrwmFYfDJd3wXdizRp\nU8/B82bod3Y7bjve0J7ntYGfjNbTzmFT9/L0JNP9A3y7dpxxJxapNwa40swuZliZvXOSVadOJlkW\nGP1zPkkemeRJ04yxHEOJwB3A1PZD/8WQdXpHKxm4hyQPSrLtQlzfh4E/AB9sOyqMj7NMkmeM/Lx5\n2yFh3GrteNss7kWatI8x/Dd5wHQLM6d2UQA+1Y77J3nkSPtSwPsZ3hcPv4+vdbFoC+eOB9YF9h5t\nS7I1Q2nRjQy7Q0gPKNbgSjOoqj8kOQQ4ADg7yVcZnp2/ZFgU8rOR7mu1PucBP2RYtPEwhq2HVgc+\nVFW3tHF/meRFDG8+pyc5AbiAYYnAOgyLxx7BsEftgq7v4rYP7qeAC5J8C7iEoVbvUQyZ3euBJ7SX\nvBJ4XZJTGbY2uhF4DMNist8B/7Go9yJNWlVdmOQNDNtmnZ3kGIZ9cB/BsOfszcB2VfX9JO8D/gk4\nv9WT38qQ+d2IoQTn4Encwyy9nmGR6MFtP94zuXsf3LuAv/M5fYBxkRlggCstrHcwZDb3BF4LXAcc\nybBQa3SHgyta320Z/jy4CvAr4EcMda5Hjg5aVSckeTLDvps7MASjv2cImk9k+BCGGVXVZ5Ocy7DN\n0XYMWwfd2sY5invuw/kF4CHAU4HNgWUZFuYcybD35vmzuRdp0qrqE0nOZ3ietmUot7mB4R9onxzp\nt0+Ss4E3MmzXtzTDP/b2Z3gGlpi/TFTV5Um2YLj2HRnu+2bgW8C7q+qMCV6eNDEZ2RVIkiRJS6g8\nZNNi9ZMmfRmDq1Y6q6q2mNT0ZnAlSZJ6UMyJHQzmAheZSZIkqStmcCVJknph5SlgBleSJEmdMcCV\nJElSVyxRkCRJ6sLc+JjcucAMriRJkrpigCvpAS3JnUnOSXJ+ki8n+bN7Mda2Sb7evv+rJPsuoO9K\n7ZO3FnWOA5O8dWHPj/X5dPv0vIWda932wQmStEQxwJX0QHd7VW1SVRsxfIrc60cbM1jk/1dW1bFV\nddACuqwELHKAK0kLVHPka8IMcCXpbqcAj22Zyx8l+QxwPrBOku2TnJZkXsv0Lg+Q5LlJLk4yD3jh\n1EBJdkvy4fb9akm+muTc9vVU4CDgMS17fHDr97YkZyT5YZJ3joz19iSXJDkVePxMN5FkzzbOuUmO\nHstKPyfJmW28F7T+SyU5eGTu193bX6QkTZIBriQBSR4MPA84r516HPDRqvpz4FZgf+A5VbUZcCbw\n5iQPBT4B7ARsDqw+n+E/BHy3qjYGNgMuAPYFLmvZ47cl2b7NuRWwCbB5kmcm2Rx4WTu3I7DlQtzO\nV6pqyzbfRcAeI23rtjmeDxza7mEP4Kaq2rKNv2eS9RZiHkmak9xFQdID3bJJzmnfnwIcDqwJXFlV\np7fz2wAbAt9LArAMcBrwBOAnVXUpQJLPAq+dZo6/AF4FUFV3AjclWXmsz/bt6+z28/IMAe8KwFer\n6rY2x7ELcU8bJXkXQxnE8sBxI21fqqq7gEuTXN7uYXvgySP1uSu2uS9ZiLkkzSXuogAY4ErS7VW1\nyeiJFsTeOnoKOL6qXj7W7x6vu5cCvLeqPj42x96zGOvTwC5VdW6S3YBtR9rGq+Oqzf0PVTUaCJNk\n3VnMLUkTZ4mCJM3sdOBpSR4LkGS5JBsAFwPrJnlM6/fy+bz+BGCv9tqlkqwI3MKQnZ1yHLD7SG3v\nWklWBU4GdkmybJIVGMohZrICcG2SpYFXjLW9OMmD2jWvD/yozb1X60+SDZIstxDzSJpLJr2wbA4t\nMjODK0kzqKrrWyb0C0ke0k7vX1WXJHkt8I0ktzGUOKwwzRBvAg5LsgdwJ7BXVZ2W5HttG65vtjrc\nJwKntQzyb4Bdq2peki8C5wK/AM5YiEs+APgBcH07jl7TVcD/AQ8DXl9Vv03ySYba3HkZJr8e2GXh\nfjuSNPekag6E2ZIkSbpXsvRmxSonT/oyBtetcFZVbTGp6c3gSpIk9cJFZoA1uJIkSeqMAa4kSZK6\nYomCJElSL1xaBZjBlSRJUmfM4EqSJPXCRWaAGVxJkiR1xgBXkiRJXbFEQZIkqRcuMgPM4EqSJKkz\nBriSJEnqigGuJElSD4phF4W58LUASdZJ8p0kFya5IMmb2vmHJzk+yaXtuPJsfxUGuJIkSbo/3QG8\npao2BLYB/j7JhsC+wAlV9TjghPbzrBjgSpIk9aLmyNeCLrHq2qqa176/BbgIWAvYGTiidTsC2GW2\nvwYDXEmSJE1EknWBTYEfAKtV1bWt6TpgtdmO6zZhkiRJWtxWSXLmyM+HVdVhox2SLA8cDexdVTcn\nd9fuVlUlmfWmZwa4kiRJXZh5gdf96Iaq2mJ+jUmWZghuP1dVX2mnf55kjaq6NskawC9mO7klCpIk\nSbrfZEjVHg5cVFUfGGk6Fnh1+/7VwDGzncMMriRJku5PTwNeCZyX5Jx27p+Bg4AvJdkDuBJ4yWwn\nMMCVJEnqxRLwUb1VdSowv1qKZy+OOSxRkCRJUlcMcCVJktQVSxQkSZJ6MXd2UZgoM7iSJEnqihlc\nSZKkHizEx+Q+UJjBlSRJUlcMcCVJktQVSxQkSZJ64SIzwAyuJEmSOmOAK0mSpK5YoiBJktQLd1EA\nzOBKkiSpM2ZwJUmSeuEiM8AMriRJkjpjgCtJkqSuWKIgSZLUCxeZAWZwJUmS1BkDXEmSJHXFEgVJ\nkqQeFO6i0JjBlSRJUlfM4EqSJPXCRWaAGVxJkiR1xgBXkiRJXbFEQZIkqQtxkVljBleSJEldMcCV\nJElSVyxRkCRJ6oW7KABmcCVJktQZA1xJkiR1xRIFSZKkXriLAmAGV5IkSZ0xgytJktSDwkVmjRlc\nSZIkdcUAV5IkSV2xREGSJKkXLjIDzOBKkiSpMwa4kiRJ6oolCpIkSb1wFwXADK4kSZI6YwZXkiSp\nFy4yA8zgSpIkqTMGuJIkSeqKJQqSJEm9cJEZYAZXkiRJnTGDK0mS1IWzjoOsMumraG6Y5OSpMpct\nSZKkfliiIEmSpK4Y4EqSJKkrBriSJEnqigGuJEmSumKAK0mSpK4Y4EqSJKkrBriSJEnqigGuJEmS\numKAK0mSpK78P/2H2eEYQrYjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_predicted_counts)\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "plot = plot_confusion_matrix(cm, classes=['useless','common'], normalize=False, title='Confusion matrix')\n",
    "plt.show()\n",
    "# print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 混淆矩阵的阅读 查看百度\n",
    "# 由上图可得模型是 漏判 问题, 错判为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 获取 逻辑回归的系数和词典 词个数相同。系数越大，代表给词权重越大\n",
    "def get_most_important_features(vectorizer, model, n=5):\n",
    "    # vectorizer 是 sklearn.feature_extraction.text.CountVectorizer 对象\n",
    "    index_to_word = {v:k for k,v in vectorizer.vocabulary_.items()}\n",
    "    \n",
    "    # loop for each class\n",
    "    classes ={}\n",
    "    for class_index in range(model.coef_.shape[0]):\n",
    "        word_importances = [(el, index_to_word[i]) for i,el in enumerate(model.coef_[class_index])]\n",
    "        sorted_coeff = sorted(word_importances, key = lambda x : x[0], reverse=True)\n",
    "        tops = sorted(sorted_coeff[:n], key = lambda x : x[0])\n",
    "        bottom = sorted_coeff[-n:]\n",
    "        classes[class_index] = {\n",
    "            'tops':tops,\n",
    "            'bottom':bottom\n",
    "        }\n",
    "    return classes\n",
    "\n",
    "importance = get_most_important_features(count_vectorizer, clf, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bottom': [(-1.9233468753654832, '想玩过'),\n",
       "  (-1.9699934849413354, '东西'),\n",
       "  (-1.9936101307589726, '一科'),\n",
       "  (-2.0586707932196653, '完整版'),\n",
       "  (-2.2143741219571784, '第六感'),\n",
       "  (-2.2944339277405317, '平台'),\n",
       "  (-2.296290346516122, '张立根'),\n",
       "  (-2.5400863161740292, '全过'),\n",
       "  (-2.6306392117308213, 'f2'),\n",
       "  (-3.1548797624472003, 'cnm')],\n",
       " 'tops': [(2.1516471108530144, 'lajibamxigyegei'),\n",
       "  (2.1803090306315083, '界面'),\n",
       "  (2.211154149794893, '根本'),\n",
       "  (2.2950592487795909, '下载'),\n",
       "  (2.507806444700754, '广告'),\n",
       "  (2.513095684570573, '不错'),\n",
       "  (2.8654346669109865, '好难'),\n",
       "  (2.9219171750081423, '样儿'),\n",
       "  (3.092310924958777, '老是'),\n",
       "  (3.2120104179278735, '不能')]}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SvBs4EngSsCbJnfqmq6vq6knUJEmSNEmTOqbshXRnXP43cNHA7eUTqkeSJGmi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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_important_words(top_scores, top_words, bottom_scores, bottom_words, name):\n",
    "    y_pos = np.arange(len(top_words))\n",
    "    top_pairs = [(a,b) for a,b in zip(top_words, top_scores)]\n",
    "    top_pairs = sorted(top_pairs, key=lambda x: x[1])\n",
    "    \n",
    "    bottom_pairs = [(a,b) for a,b in zip(bottom_words, bottom_scores)]\n",
    "    bottom_pairs = sorted(bottom_pairs, key=lambda x: x[1], reverse=True)\n",
    "    \n",
    "    top_words = [a[0] for a in top_pairs]\n",
    "    top_scores = [a[1] for a in top_pairs]\n",
    "    \n",
    "    bottom_words = [a[0] for a in bottom_pairs]\n",
    "    bottom_scores = [a[1] for a in bottom_pairs]\n",
    "    \n",
    "    fig = plt.figure(figsize=(10, 10))  \n",
    "\n",
    "    plt.subplot(121)\n",
    "    plt.barh(y_pos,bottom_scores, align='center', alpha=0.5)\n",
    "    plt.title('useless', fontsize=20)\n",
    "    plt.yticks(y_pos, bottom_words, fontsize=14)\n",
    "    plt.suptitle('Key words', fontsize=16)\n",
    "    plt.xlabel('Importance', fontsize=20)\n",
    "    \n",
    "    plt.subplot(122)\n",
    "    plt.barh(y_pos,top_scores, align='center', alpha=0.5)\n",
    "    plt.title('common', fontsize=20)\n",
    "    plt.yticks(y_pos, top_words, fontsize=14)\n",
    "    plt.suptitle(name, fontsize=16)\n",
    "    plt.xlabel('Importance', fontsize=20)\n",
    "    \n",
    "    plt.subplots_adjust(wspace=0.8)\n",
    "    plt.show()\n",
    "\n",
    "top_scores = [a[0] for a in importance[0]['tops']]\n",
    "top_words = [a[1] for a in importance[0]['tops']]\n",
    "bottom_scores = [a[0] for a in importance[0]['bottom']]\n",
    "bottom_words = [a[1] for a in importance[0]['bottom']]\n",
    "\n",
    "plot_important_words(top_scores, top_words, bottom_scores, bottom_words, \"Most important words for relevance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tfidf(data):\n",
    "    tfidf_vectorizer = TfidfVectorizer()\n",
    "\n",
    "    train = tfidf_vectorizer.fit_transform(data)\n",
    "\n",
    "    return train, tfidf_vectorizer\n",
    "\n",
    "X_train_tfidf, tfidf_vectorizer = tfidf(X_train)\n",
    "X_test_tfidf = tfidf_vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min x:-3.02791925882e-09, max x:0.746021068178\n",
      "min y:-0.175363138076, max y:0.921014086479\n"
     ]
    },
    {
     "data": {
      "image/png": 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JYICVJEmSJCWCAVaSJEmSlAgGWEmSJElSIhhgJUmSJEmJYICVJEmSJCWCAVaSJEmSlAgG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\n32c62nK+76GJddfpSY2Wa0KdtXrddZ5jtzG7F6m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WNt+XAfaBBwpfny/jsu/RF6Gh/gyM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\nw/MuBDnpNtZ9alOb//enPyJdvtxO8l69LKlGs2b+rI6+RmZ8vL+x/vz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32vtd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\nIfwPrcLXQUrQrIAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 16))  \n",
    "plt.axis([0,30,-2,2]) \n",
    "plot_LSA(X_train_tfidf, y_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf_tfidf = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg', \n",
    "                         multi_class='multinomial', n_jobs=-1, random_state=40)\n",
    "clf_tfidf.fit(X_train_tfidf, y_train)\n",
    "\n",
    "y_predicted_tfidf = clf_tfidf.predict(X_test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 0.886, precision = 0.887, recall = 0.886, f1 = 0.887\n"
     ]
    }
   ],
   "source": [
    "accuracy_tfidf, precision_tfidf, recall_tfidf, f1_tfidf = get_metrics(y_test, y_predicted_tfidf)\n",
    "print(\"accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f\" % (accuracy_tfidf, precision_tfidf, \n",
    "                                                                       recall_tfidf, f1_tfidf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fb13kNe2R5zo9dsHCJdw96wUARo0MNlxj+XbHufXRBSzuokvYTTNaz7XxeANc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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TFIDF confusion matrix\n",
      "[[ 66  12]\n",
      " [ 14 137]]\n",
      "BoW confusion matrix\n",
      "[[ 67  11]\n",
      " [ 15 136]]\n"
     ]
    }
   ],
   "source": [
    "cm2 = confusion_matrix(y_test, y_predicted_tfidf)\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "plot = plot_confusion_matrix(cm2, classes=['useless','common'], normalize=False, title='Confusion matrix')\n",
    "plt.show()\n",
    "print(\"TFIDF confusion matrix\")\n",
    "print(cm2)\n",
    "print(\"BoW confusion matrix\")\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bottom': [(-2.214806650606687, '完整版'),\n",
       "  (-2.2968718774370003, '平台'),\n",
       "  (-2.3906277130396494, '25'),\n",
       "  (-2.5190525360319769, '东西'),\n",
       "  (-2.5285441951207162, '张立根'),\n",
       "  (-2.5486040537728112, '第六感'),\n",
       "  (-2.5690698501328586, '全过'),\n",
       "  (-2.9121577550201523, 'f2'),\n",
       "  (-2.9606557249621637, 'cnm'),\n",
       "  (-3.0555485879995237, '怎么')],\n",
       " 'tops': [(2.8541240993039523, '界面'),\n",
       "  (2.9206942310168413, '可以'),\n",
       "  (2.9828328519493703, '样儿'),\n",
       "  (3.2951901279469049, '好难'),\n",
       "  (3.3947341973882685, '根本'),\n",
       "  (3.6632320779813283, '不过'),\n",
       "  (3.7526607158843639, '下载'),\n",
       "  (3.7721366851475926, '不错'),\n",
       "  (4.0426384252248146, '广告'),\n",
       "  (4.0993815170005261, '不能')]}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance_tfidf = get_most_important_features(tfidf_vectorizer, clf_tfidf, 10)\n",
    "importance_tfidf[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from gensim.models import KeyedVectors\n",
    "word2vec_path = \"qq_comments_vector\"\n",
    "word2vec = KeyedVectors.load(word2vec_path, mmap='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_average_word2vec(tokens_list, vector, generate_missing=False, k=100):\n",
    "    if len(tokens_list)<1:\n",
    "        return np.zeros(k)\n",
    "    if generate_missing:\n",
    "        vectorized = [vector[word] if word in vector else np.random.rand(k) for word in tokens_list]\n",
    "    else:\n",
    "        vectorized = [vector[word] if word in vector else np.zeros(k) for word in tokens_list]\n",
    "    length = len(vectorized)\n",
    "    # print(length)\n",
    "    # print(vectorized)\n",
    "    # vectorized = [[1,2,3......100],[1,2,3,.....100],......[1,2,3.......100]] 一个句子n词语，就有 n行\n",
    "    # np.sum(vectorized, axis=0) 每列相加， 即使每个句子的词个数不同，这样处理，最终都是（1,100）向量\n",
    "    # 但是必须求平均值，否则肯定不公平，这样就把一个句子 doc2vec 表示出来了\n",
    "    summed = np.sum(vectorized, axis=0)\n",
    "    averaged = np.divide(summed, length)\n",
    "    return averaged\n",
    "\n",
    "def get_word2vec_embeddings(vectors, clean_questions, generate_missing=False):\n",
    "    embeddings = clean_questions['tokens'].apply(lambda x: get_average_word2vec(x, vectors, generate_missing=generate_missing))\n",
    "    return list(embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_tokens(x):\n",
    "    return [i for i in x.split(' ')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# a = [[1,2,3],[2,3,4]]\n",
    "# np.sum(a, axis=0)\n",
    "# av = np.divide(b, 2)\n",
    "# av"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_comment['tokens'] = df_comment['jieba_word'].apply(get_tokens)\n",
    "# get_word2vec_embeddings 句子由n个词组成，每个词有（1,100）向量组成，\n",
    "# 注意 100，要和 word2vec 训练库的参数一样，就是约定把，每个词表示成一个（1,100）的向量\n",
    "embeddings = get_word2vec_embeddings(word2vec, df_comment)\n",
    "X_train_word2vec, X_test_word2vec, y_train_word2vec, y_test_word2vec = train_test_split(embeddings, list_labels, \n",
    "                                                                                        test_size=0.2, random_state=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min x:-0.84088147382, max x:11.1237220542\n",
      "min y:-10.6047643796, max y:12.6548173101\n"
     ]
    },
    {
     "data": {
      "image/png": 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SpHbJoCq1dR36wSF/T7qKtq96IRCHdbgN66F+DSNGVHLaafCXv8C++8IJaV+S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\nTmN1WMccFW1pHgYTDh3O8AG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qqqSrkSSpbXBEVW3Y2+/DxOEpKkyskjat42CgIITUqAg6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\ngEH5P/5XZ3gThg3jAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 16))          \n",
    "plot_LSA(embeddings, list_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf_w2v = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg', \n",
    "                         multi_class='multinomial', random_state=40)\n",
    "clf_w2v.fit(X_train_word2vec, y_train_word2vec)\n",
    "y_predicted_word2vec = clf_w2v.predict(X_test_word2vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 0.860, precision = 0.859, recall = 0.860, f1 = 0.858\n"
     ]
    }
   ],
   "source": [
    "accuracy_word2vec, precision_word2vec, recall_word2vec, f1_word2vec = get_metrics(y_test_word2vec, y_predicted_word2vec)\n",
    "print(\"accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f\" % (accuracy_word2vec, precision_word2vec, \n",
    "                                                                       recall_word2vec, f1_word2vec))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LGs6j+DYA4HuZeVdmPgN8ody3DHB5G/4AkrSEDHCl3u0oGoOs7SnqQber94CI2CYibqJY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\no3W2WKgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word2Vec confusion matrix\n",
      "[[ 58  20]\n",
      " [ 12 139]]\n",
      "TFIDF confusion matrix\n",
      "[[ 66  12]\n",
      " [ 14 137]]\n",
      "BoW confusion matrix\n",
      "[[ 67  11]\n",
      " [ 15 136]]\n"
     ]
    }
   ],
   "source": [
    "cm_w2v = confusion_matrix(y_test_word2vec, y_predicted_word2vec)\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "plot = plot_confusion_matrix(cm_w2v, classes=['useless','common'], normalize=False, title='Confusion matrix')\n",
    "plt.show()\n",
    "print(\"Word2Vec confusion matrix\")\n",
    "print(cm_w2v)\n",
    "print(\"TFIDF confusion matrix\")\n",
    "print(cm2)\n",
    "print(\"BoW confusion matrix\")\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2195 unique tokens.\n",
      "(2196, 100)\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "# embedding 维度\n",
    "EMBEDDING_DIM = 100\n",
    "MAX_SEQUENCE_LENGTH = max(df_comment['tokens'].apply(len))\n",
    "# 要统计总词汇量\n",
    "from gensim import corpora\n",
    "words = [x for x in df_comment['tokens']]\n",
    "dic = corpora.Dictionary(words)\n",
    "VOCAB_SIZE = len(dic)\n",
    "\n",
    "VALIDATION_SPLIT=0.2\n",
    "# num_words 是保留的最大词汇数\n",
    "tokenizer = Tokenizer(num_words=VOCAB_SIZE)\n",
    "tokenizer.fit_on_texts(df_comment['jieba_word'].tolist())\n",
    "sequences = tokenizer.texts_to_sequences(df_comment['jieba_word'].tolist())\n",
    "\n",
    "word_index = tokenizer.word_index\n",
    "print('Found %s unique tokens.' % len(word_index))\n",
    "\n",
    "# 将 sequence 列表的列表 变成 2d矩阵 numpy shape = (样本数，默认为max的句子词数) \n",
    "cnn_data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\n",
    "# cnn_data = pad_sequences(sequences)\n",
    "labels = to_categorical(np.asarray(df_comment['type']))\n",
    "\n",
    "indices = np.arange(cnn_data.shape[0])\n",
    "# 随机排序 shuffle\n",
    "np.random.shuffle(indices)\n",
    "cnn_data = cnn_data[indices]\n",
    "labels = labels[indices]\n",
    "# cnn_data 是shape=(样本数目，每条文本词汇对应字典的序号) 且列数统一补充为最长句子的长度\n",
    "\n",
    "num_validation_samples = int(VALIDATION_SPLIT * cnn_data.shape[0])\n",
    "\n",
    "embedding_weights = np.zeros((len(word_index)+1, EMBEDDING_DIM))\n",
    "for word,index in word_index.items():\n",
    "    embedding_weights[index,:] = word2vec[word] if word in word2vec else np.random.rand(EMBEDDING_DIM)\n",
    "print(embedding_weights.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  0.,  0.],\n",
       "       [ 1.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  1.],\n",
       "       [ 1.,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_categorical([1,0,3,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2195"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print(df_comment['jieba_word'][0])\n",
    "# print(word_index['的'])\n",
    "# sequences[0]\n",
    "# np.random.shuffle(np.asarray([0,1,2]))\n",
    "# int(23.3)\n",
    "# np.zeros((1,2))\n",
    "# print(df_comment['type'].unique())\n",
    "len(word_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.layers import Dense, Input, Flatten, Dropout, Merge\n",
    "from keras.layers import Conv1D, MaxPooling1D, Embedding\n",
    "from keras.layers import LSTM, Bidirectional\n",
    "from keras.models import Model\n",
    "# embeddings 是每个词的向量形式构成的矩阵， \n",
    "# embedding_dim 是word2vec 每个词向量的维数\n",
    "# num_words 为什么要+1\n",
    "def ConvNet(embeddings, max_sequence_length, num_words, embedding_dim, labels_index, trainable=False, extra_conv=True):\n",
    "    embedding_layer = Embedding(num_words,\n",
    "                            embedding_dim,\n",
    "                            weights=[embeddings],\n",
    "                            input_length=max_sequence_length,\n",
    "                            trainable=trainable)\n",
    "    print(\"num_words:\",num_words)\n",
    "    print(\"embedding_dim:\",embedding_dim)   \n",
    "    print(\"input_length：\",max_sequence_length)\n",
    "    print(embedding_layer)\n",
    "    sequence_input = Input(shape=(max_sequence_length,), dtype='int32')\n",
    "    embedded_sequences = embedding_layer(sequence_input)\n",
    "    \n",
    "    # Yoon Kim model (https://arxiv.org/abs/1408.5882)\n",
    "    convs = []\n",
    "    filter_sizes = [3,4,5]\n",
    "    # 下面应用了大量的函数式编程\n",
    "    for filter_size in filter_sizes:\n",
    "        l_conv = Conv1D(filters=128, kernel_size=filter_size, activation='relu')(embedded_sequences)\n",
    "        print(filter_size,\":\",l_conv)\n",
    "        l_pool = MaxPooling1D(pool_size=3)(l_conv)\n",
    "        convs.append(l_pool)\n",
    "\n",
    "    l_merge = Merge(mode='concat', concat_axis=1)(convs)\n",
    "\n",
    "    # add a 1D convnet with global maxpooling, instead of Yoon Kim model\n",
    "    conv = Conv1D(filters=128, kernel_size=3, activation='relu')(embedded_sequences)\n",
    "    pool = MaxPooling1D(pool_size=3)(conv)\n",
    "\n",
    "    if extra_conv==True:\n",
    "        x = Dropout(0.5)(l_merge)  \n",
    "    else:\n",
    "        # Original Yoon Kim model\n",
    "        x = Dropout(0.5)(pool)\n",
    "    x = Flatten()(x)\n",
    "    x = Dense(128, activation='relu')(x)\n",
    "    #x = Dropout(0.5)(x)\n",
    "\n",
    "    preds = Dense(labels_index, activation='softmax')(x)\n",
    "\n",
    "    model = Model(sequence_input, preds)\n",
    "    # 编译\n",
    "    model.compile(loss='categorical_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['acc'])\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_words: 2196\n",
      "embedding_dim: 100\n",
      "input_length： 65\n",
      "<keras.layers.embeddings.Embedding object at 0x7f16d438b940>\n",
      "3 : Tensor(\"conv1d_9/Relu:0\", shape=(?, 63, 128), dtype=float32)\n",
      "4 : Tensor(\"conv1d_10/Relu:0\", shape=(?, 62, 128), dtype=float32)\n",
      "5 : Tensor(\"conv1d_11/Relu:0\", shape=(?, 61, 128), dtype=float32)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mingchao/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:31: UserWarning: The `Merge` layer is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n"
     ]
    }
   ],
   "source": [
    "# 取样本比例0.8为训练集，0.2为测试集 \n",
    "# 注意 CNN 的输出是一个 向量了，不是\n",
    "x_train = cnn_data[:-num_validation_samples]\n",
    "y_train = labels[:-num_validation_samples]\n",
    "x_val = cnn_data[-num_validation_samples:]\n",
    "y_val = labels[-num_validation_samples:]\n",
    "# +1 是因为 \n",
    "model = ConvNet(embedding_weights, MAX_SEQUENCE_LENGTH, len(word_index)+1, EMBEDDING_DIM, \n",
    "                len(list(df_comment['type'].unique())), False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 916 samples, validate on 228 samples\n",
      "Epoch 1/3\n",
      "916/916 [==============================] - 0s 57us/step - loss: 0.1688 - acc: 0.9334 - val_loss: 0.2489 - val_acc: 0.8640\n",
      "Epoch 2/3\n",
      "916/916 [==============================] - 0s 58us/step - loss: 0.1379 - acc: 0.9552 - val_loss: 0.2334 - val_acc: 0.8816\n",
      "Epoch 3/3\n",
      "916/916 [==============================] - 0s 66us/step - loss: 0.1087 - acc: 0.9607 - val_loss: 0.2545 - val_acc: 0.8728\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f1729aa09e8>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=3, batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_3 (InputLayer)            (None, 65)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_4 (Embedding)         (None, 65, 100)      219600      input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_9 (Conv1D)               (None, 63, 128)      38528       embedding_4[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_10 (Conv1D)              (None, 62, 128)      51328       embedding_4[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_11 (Conv1D)              (None, 61, 128)      64128       embedding_4[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_9 (MaxPooling1D)  (None, 21, 128)      0           conv1d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_10 (MaxPooling1D) (None, 20, 128)      0           conv1d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_11 (MaxPooling1D) (None, 20, 128)      0           conv1d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "merge_3 (Merge)                 (None, 61, 128)      0           max_pooling1d_9[0][0]            \n",
      "                                                                 max_pooling1d_10[0][0]           \n",
      "                                                                 max_pooling1d_11[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 61, 128)      0           merge_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "flatten_3 (Flatten)             (None, 7808)         0           dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 128)          999552      flatten_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_6 (Dense)                 (None, 2)            258         dense_5[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 1,373,394\n",
      "Trainable params: 1,153,794\n",
      "Non-trainable params: 219,600\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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